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Modern approaches to a problem of NPP automatic regulators parameters setting
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Procedia Computer Science 145 (2018) 635–640
Postproceedings of the 9th Annual International Conference on Biologically Inspired Cognitive Postproceedings of the 9thBICA Annual International Conference Inspired Cognitive Architectures, 2018 (Ninth Annual MeetingonofBiologically the BICA Society) Architectures, BICA 2018 (Ninth Annual Meeting of the BICA Society)
Modern approaches to a problem of NPP automatic regulators Modern approaches to a problem of NPP automatic regulators parameters setting parameters setting
a
Vasilij Volodinaa*,, Andrej Tolokonskijaa, Ajk Verdyanbb Vasilij Volodin *,, Andrej Tolokonskij , Ajk Verdyan
Department of Automatics, Institute of Nuclear Physics and Engineering, National Nuclear Research University MEPhI, 31 Kashirskoe shosse, 115409, RussiaNuclear Research University MEPhI, 31 Kashirskoe shosse, Department of Automatics, Institute of Nuclear Physics andMoscow, Engineering, National b JSC “Atomenergoproekt”,Moscow, 7 h. 1 Bakuninskaya st. st., Moscow, 107996, Russia 115409, Russia b JSC “Atomenergoproekt”, 7 h. 1 Bakuninskaya st. st., Moscow, 107996, Russia
a
Abstract Abstract Purpose of I&C (instrumentation and control) system is monitor and control of processing procedure and equipment for reaching main goal nuclear power plantand (NPP) – electric generation providing nuclear and radiological safety and Purpose of of I&C (instrumentation control) systemenergy is monitor and control of processing procedure and equipment foreconomic reaching efficiency processing procedure. One –ofelectric the main functions of NPP I&C system is and control of technological processes in main goal of nuclear power plant (NPP) energy generation providing nuclear radiological safety and economic nuclear unitofequipment automaticOne regulators optimality of electric energyisgeneration Algorithms of I&C efficiency processingvia procedure. of thes providing main functions of NPP I&C system control ofprocess. technological processes in systems, especially lawsviaofautomatic control, regulators are realized on hardware and of software complex TPTS. A process. great number umber of automatic nuclear unit equipment s providing optimality electriccomp energy generation Algorithms of I&C regulators are used inlaws process of electric generation on NPP. nuclear unitsTPTS. are standard (they have of same reactor systems, especially of control, areenergy realized on hardware andDespite software comp complex A great number umber automatic system project), have unique characteristics – nuclear units Despite differ by technological of reactor system regulators are usedthey in process of electric energy generation on NPP. nuclear units are characteristics standard (they have same reactor equipment and technological equipment providing– electr electric ic energy generation process control. That is why parameters of system project), they have unique characteristics nuclear units differ by technological characteristics of reactor system respective regulators equipment for each nuclear unit are corrected aftergeneration theoreticalprocess calculations in nuclear checkout pprocess rocess equipment automatic and technological providing electr electric ic energy control. That isunit why parameters of that cause automatic specific economic losses. solving ng this problem in this paper modern methods NPP automatic respective regulatorsand forenergetic each nuclear unitFor are solvi corrected after theoretical calculations in nuclear unitofcheckout pprocess rocess regulators setting are as For “intelligent” control system on neural networks and and that cause parameters specific economic andconsidered energetic such losses. solvi solving ng this problem in (based this paper modern methods offuzzy NPP logic) automatic using of analytical simulators of considered NPP nuclear units, which are us used ed for system researches of reactor system physics researches of regulators parameters setting are such as “intelligent” control (based on neural networks andand fuzzy logic) and behavior of technological equipment nuclearwhich unit. are us using of analytical simulators of NPPrespective nuclear units, used ed for researches of reactor system physics and researches of behavior of technological equipment respective nuclear unit. © 2019 The Authors. Published by Elsevier B.V. © 2018 The Authors. Published by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND © 2019 The Authors. by Elsevier B.V. ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review review under responsibility of the the scientific committee of the 9th Annual International Conference on Biologically Inspired This is an open access article under BY-NC-ND ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the CC scientific committee of the 9th Annual International Conference on Biologically Inspired Cognitive Architectures. Peer-review review under responsibility of the scientific committee of the 9th Annual International Conference on Biologically Inspired Cognitive Architectures. Cognitive Architectures.
Keywords: nuclear power plant; control system; fuzzy logic; neural network; analytical simulator; automatic regulator Keywords: nuclear power plant; control system; fuzzy logic; neural network; analytical simulator; automatic regulator
* Corresponding author. Tel.: +7-916-004-39-50. E-mail address:author. [email protected] * Corresponding Tel.: +7-916-004-39-50. E-mail address: [email protected] 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access under the CC by BY-NC-ND NDB.V. license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2019 The article Authors. Published Elsevier Peer-review review under responsibility of the committee of the(https://creativecommons.org/licenses/by-nc-nd/4.0/) 9th Annual International Conference on Biologically Inspired C Cognitive ognitive This is an open access article under thescientific CC BY-NC-ND ND license Architectures. Peer-review review under responsibility of the scientific committee of the 9th Annual International Conference on Biologically Inspired C Cognitive ognitive Architectures. 1877-0509 © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 9th Annual International Conference on Biologically Inspired Cognitive Architectures. 10.1016/j.procs.2018.11.070
Vasilij Volodin et al. / Procedia Computer Science 145 (2018) 635–640 Vasilij Volodin et al. / Procedia Computer Science 00 (2019) 000–000
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1. Introduction Nomenclature GET-R HSC I&C MCR NPP PID SG TPTS
computer-aided design system of algorithms of instrumentation and control system of nuclear power plants hardware and software complex Instrumentation and Control main control room nuclear power plant proportional-integral-differential regulator steam generator hardware and software tools for instrumentation and control system applied at nuclear power plants and developed by Dukhov All-Russian Scientific Research Institute of Automation
Each NPP unit has digital control and instrumentation system. Purpose of NPP I&C system is monitor and control of processing procedure and equipment for reaching main goal of NPP – electric energy generation providing nuclear and radiological safety and economic efficiency of processing procedure [1]. I&C system includes control subsystems divided by functional purpose. I&C system has hierarchical structure and consists of following levels: level of liaising with technological object of control, level of lower automation, top level (human-machine interface) – top unit level of monitor and control. Most of the algorithms of NPP I&C systems, especially laws of control, are realized on hardware and software complex TPTS through computer-aided design system GET-R [2]. Functional modules racks perform acquisition and primary processing of input analog and discrete signals from sensors of technological parameters, proceed necessary calculations, and also perform automatic regulator and feedback and remote control such actuators as pump, slide-type valve, solenoid coil, switch, control valve. One of the main functions of NPP I&C system is control of technological processes in nuclear unit equipment via automatic regulators providing optimality of electric energy generation process. Automatic regulators provide keeping of regulating quantity in prescribed limits in stationary modes and desired characteristics of control performance in transient modes [3]. 2. Automatic regulators of NPP A great number of automatic regulators are used in process of electric energy generation on NPP. All automatic regulators can be divided into 4 groups. Automatic regulators providing control of reactor power, turbine power and generator power in basic mode and in tracking mode belong to the first group. Automatic regulators providing preset value of technological parameters of nuclear unit in transit modes caused by emergency protection system or preventive protection system belong to the second group. Automatic regulators providing preset parameters of steam generators (SG) feeding on the power levels close to nominal belong to the third group. Automatic regulators providing preset technological parameters in routine shots and shutdowns of nuclear unit including service steam belong to the fourth group. For example, this division for nuclear unit with fast fission reactor looks like this: Main automatic regulators: automatic regulator of nuclear unit power, automatic regulator of reactor power, automatic regulator of reactor coolant pump rate of turn, automatic regulator of SG feeding, automatic regulator of turbine rotor rate of turn, automatic regulator of steam pressure before turbine valves; Emergency and stand-by mode automatic regulators: automatic regulators of pressure of fast reduction system with discharge of steam into the condenser, automatic regulators of Na temperature on the output of secondary heat exchanger; Automatic regulators of SG feeding parameters: automatic regulator of deaerator level, automatic level regulator of the third step low pressure heater and etc.; Starting and own needs automatic regulators: starting automatic regulator of SG feeding, automatic regulator of pressure in evaporator plant collector etc.
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3. Tuning of automatic regulators of NPP units Set of procedures is carried out before acceptance of NPP unit in industrial operation, including prelaunch works, physical and power launches [4]. Operation of all subsystems of NPP I&C system is checked as part of prelaunch works (also in trial performance) with fixation and reduction of all identified issues, which includes tuning of NPP units automatic regulators parameters, which performs manually [5]. NPP unit is a complex control object in view of difficulty of electricity generation technological process, a great number of control technological parameters and equipments, and also in view of high requirements for quality and reliability of NPP I&C system [6]. Also regulators manual tuning process is complicated by the fact, that NPP I&C system is multi-loop control system, i.e. there are cross couplings between different control loops [7]. Coupling between control loop of neutron flux power and control loop of generator output of turbine ([8]) or coupling between control loop of feed water flow and control loop of pH index of feed water before coagulation contact tank ([9]) are fine examples of last statement. Consequently, problem of creation of methods of NPP units automatic regulators parameters tuning arises in order to reduce input time of NPP unit into industrial operation. Authors consider applicability of approaches to a problem solution, based on fuzzy logic, neural networks and analytical simulators of NPP units. 4. Methods of tuning of automatic regulators of NPP units 4.1. Fuzzy logic and neural networks Despite the fact that basis of “intelligent” control systems was invented by L. Zadeh in 1965 [10], concept of fuzzy logic and neural networks was not popular for NPP I&C system design neither in USSR and Russian Federation nor in foreign countries. However, proven experience of usage of “intelligent” control systems in other branches of industry ([11]) caused a wave of interest to this methods under their usage in NPP I&C system designing. There are mostly papers, in which investigates applicability of given methods to certain subsystems of NPP I&C system designing – for example, control system of feed water level in SG of control system of neutron flux power [12-14]. There are two ways of fuzzy logic and neural networks usage in NPP I&C system designing [15]. The first is the design of fuzzy regulator or the regulator, based on neural network [13, 14, 16-19]. However, this way connected with actual HSC of NPP I&C system replacement, therefore this method does not cause interest within the framework of problem under consideration. The second way is design of parameters tune module of classical PIDregulators used on NPP, based on neural network and fuzzy logic [12, 20]. However, NPP unit is complex, multiloop and nonlinear control object; therefore, it is impossible to consider all disturbances, which significantly influence on quality of control process. Another difficulty is in fact, that automatic regulators must be set up for all operation modes of NPP unit – nominal level of power, minimal controlled level of power etc. In view of difficulty of given problem two regulators (starting regulator and mandatory regulator) are used in some control loops – for example, control loop of feed water level in SG or control loop of coolant level in pressurizer. Another difficulty of NPP unit automatic regulators tuning is in fact, that each NPP unit is unique because typical units (with same reactor plant project) differ in technological equipment characteristics and quantity, in arrangement of adjusting controls and measure points that lead to arising of problem under consider for every unit. Abovementioned difficulties lead to the fact that a great number of linguistic rules, formed by expert, will require for record all cross couplings between control loops and for providing required control quality in all power ranges of NPP unit via fuzzy logic. Same difficulties appears also in the approach, based on neural networks. Besides “intelligent” control systems should be used with no full information about control object and its dynamics, however, it is not so in case of NPP – technical project and safety justification report with full information about control object are developed for each NPP unit. Therefore, authors propose following approach to a problem solution. 4.2. Analytical and full sized simulators Another approach to the considered problem is usage of analytical and full sized simulators of NPP units, which are used for researches of reactor system physics and researches of behaviour of technological equipment respective nuclear unit. For each unit his own analytical simulator with considering his unique characteristics is developed:
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parameters of nuclear reactor core, quantity and power of reactor coolant pumps, delivery pumps, condenser pumps, base areas and heights of reservoirs, characteristics of transformers and turbine, parameters of SG etc. Analytical simulator – hardware and software modelling complex, which is developed for training and proficiency maintaining of NPP main control room (MCR) operators, with using full sized mathematical model of nuclear unit working in real time mode. There is an analytical simulator of reserve control room of Kalininskaya NPP unit 4 on figure 1.
Fig. 1 – Analytical simulator of reserve control room of Kalininskaya NPP unit 4
In addition, for automatic regulators settings it is suggested to use full sized simulators of nuclear units because they contain mathematical model similar to mathematical model of analytical simulator working in real time mode. Full sized simulator – hardware and software modelling complex, which is developed for professional training of NPP MCR operators with using full sized model of MCR and complex all mode mathematical model of nuclear unit working in real time mode. The full sized simulator should correspond to the systems and equipment of nuclear unitprototype and provide in real time mode simulation of all operating modes of NPP (normal operation modes, transient modes, normal operation failures, design basis accidents and non-project accidents to simulation limits). There is full sized simulator of Kalininskaya NPP unit 4 containing workstations of reactor part chief engineer and turbine part chief engineer with respective segmented console, and workstation of unit supervisor on figure 2.
Fig. 2 – Full sized simulator of Kalininskaya NPP unit 4
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Approach to a NPP unit automatic regulators parameters tuning via simulators is the following. It is necessary to develop software, which will integrate into HSC of analytical simulator of respective NPP unit. Parametric identification of control object and following NPP units automatic regulators parameters setting with methods of optimal control theory in accordance with pre-set performance control criterion of technological processes on NPP unit will be performed via this software. Advantage of this approach is in fact, that developed method will be universal and verified on respective analytical simulators. “Intelligent” control systems also can be verified on this simulators, but high complexity of designing of linguistic rules for all power ranges of NPP unit makes this approach possible for some subsystems of NPP I&C system, but not for all regulators of NPP unit. 5. Conclusion Timeliness of considered problem is in fact, that there are no methods of NPP units automatic regulators parameters setting before checkout process. For solving this problem “intelligent” control systems, based on fuzzy logic and neural networks, and analytical and full sized simulators of NPP units can be used. NPP unit is the complex, nonlinear and multi-loop control object that lead to high complexity of making of linguistic rules for control systems, based on fuzzy logic, and high difficulty of neural network setting. In view of uniqueness of typical units is suggested to set up automatic regulators parameters via respective analytical simulator for each NPP unit because usage of modules of fuzzy logic and neural networks for this problem is greatly complicated by differences of characteristics and quantity of technological equipment. In farther works, it is assumed to establish correspondence between automatic regulators parameters and parameters of technological equipment, which is control objects, also to develop methods of regulators parameters tuning and software for their realization. This approach will allow tuning NPP automatic regulators without energetic and economic losses, which is occurred due to manual tuning of NPP regulators in NPP checkout process. References [1] Zverkov V.V. Avtomatizirovannaya sistema upravleniya tehnologicheskimi protsessami AES [Automated process control system for nuclear power plants]. Moscow: NIYaU MIFI Publ., 2014. [2] Zverkov V.V. Programmno-tehnicheskiye kompleksy ASU TP AES. Funktsionalnye i strukturnye resheniya [Hardware and software complexes of NPP I&C system]. Moscow: NIYaU MIFI Publ., 2018. [3] Potapenko P.T. Dinamika yadernogo reaktora [Dynamics of nuclear reactor]. Moscow: MIFI Publ., 1989. [4] Ostrejkovskij V.A. Ekspluatatsiya atomnyh stantsij [Operation of nuclear power plants]. Moscow: Energoatomizdat Publ., 1999. [5] Saakov E.S., Ryasnyj S.I. Vvod v ekspluatatsiyu energoblokov AES [Start-up of nuclear power plant units]. Moscow: Energoatomizdat Publ., 2007. [6] Stefani E.P. Osnovy postrojeniya ASU TP [Basics of I&C system design]. Moscow: Energoatomizdat Publ., 1982. [7] Tsykunov A.M. Robastnoye upravleniye obyektami s posledejstviem [Robust control of objects with persistent]. Moscow: FIZMATLIT Publ., 2014. [8] Vygovskij S.B., Ryabov N.O., Semenov A.A., Chernov E.V., Bogachek L.N. Fizicheskiye i konstruktsionnye osobennosti yadernyh energeticheskih ustanovok s VVER [Physical and construction features of nuclear power plants with VVER]. Moscow: NIYaU MIFI Publ., 2011. [9] Petrova T.I., Voronov V.N., Larin B.M. Tehnologiya organizatsii vodno-himicheskogo rezhima atomnyh elektrostantsij [Technology of nuclear power plants water chemistry organization]. Moscow: Izdatelskij dom MEI Publ., 2012. [10] Zade L. Ponyatie lingvisticheskoj peremennoj i ego priminenie k prinyatiyu priblizhennyh reshenij [Fuzzy sets]. Moscow: Mir Publ., 1976. [11] Pegat A. Nechetkoe modelirovanie i upravlenie [Fuzzy modeling and control]. Moscow: BINOM. Laboratoriya znanij Publ., 2018. [12] Al Masri Husam Fayiz (2017) “Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants.” J. of Physics: Conf. Series 781: 0112052. [13] Bartosz Puchalski, Kazimierz Duzinkiewicz, and Tomasz Rutkowski (2015) “Multi-region fuzzy logic controller with local PID controllers for U-tube steam generator in nuclear power plant.” Archives of Control Sciences 25 (4): 429-444. [14] Arab-Alibeik H., Setayeshi S. (2005) “Adaptive control of a PWR core power using neural networks.” Annals of Nuclear Energy 32: 588605. [15] Denisenko V.V. Kompyuternoe upravlenie tehnologichesckim protsessom, eksperimentom, oborudovaniem [Computer control of technological process, experiment and equipment]. Moscow: Goryachaya liniya – Telekom Publ., 2014. [16] Chris Brown, Hossam A. Gabbar (2014) “Fuzzy logic control for improved pressurizer systems in nuclear power plants.” Annals of Nuclear Energy 72: 461-466.
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[17] Mauro Vitor de Oliveira, José Carlos Soares de Almeida (2013) “Application of artificial intelligence techniques in modeling and control of a nuclear power plant pressurizer system.” Progress in Nuclear Energy 63: 71-85. [18] Jun-Jen Lu, Hsuan-Han Huang, Hwai-Pwu Chou (2015) “Evaluation of an FPGA-based fuzzy logic control of feed-water for ABWR under automatic power regulating.” Progress in Nuclear Energy 79: 22-31. [19] Habibiyan H., Setayeshi S., Arab-Alibeik H. (2004) “A fuzzy-gain-scheduled neural controller for nuclear steam generators.” Annals of Nuclear Energy 31: 1765-1781. [20] Cheng Liu, Jin-Feng Peng, Fu-Yu Zhao, Chong Li (2009) “Design and optimization of fuzzy-PID controller for the nuclear reactor power control.” Nuclear Engineering and Design 239: 2311-2316.
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Procedia online Computer 00 (2019) 000–000 Available at Science www.sciencedirect.com Procedia Computer Science 00 (2019) 000–000
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www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Computer Science 145 (2018) 635–640
Postproceedings of the 9th Annual International Conference on Biologically Inspired Cognitive Postproceedings of the 9thBICA Annual International Conference Inspired Cognitive Architectures, 2018 (Ninth Annual MeetingonofBiologically the BICA Society) Architectures, BICA 2018 (Ninth Annual Meeting of the BICA Society)
Modern approaches to a problem of NPP automatic regulators Modern approaches to a problem of NPP automatic regulators parameters setting parameters setting
a
Vasilij Volodinaa*,, Andrej Tolokonskijaa, Ajk Verdyanbb Vasilij Volodin *,, Andrej Tolokonskij , Ajk Verdyan
Department of Automatics, Institute of Nuclear Physics and Engineering, National Nuclear Research University MEPhI, 31 Kashirskoe shosse, 115409, RussiaNuclear Research University MEPhI, 31 Kashirskoe shosse, Department of Automatics, Institute of Nuclear Physics andMoscow, Engineering, National b JSC “Atomenergoproekt”,Moscow, 7 h. 1 Bakuninskaya st. st., Moscow, 107996, Russia 115409, Russia b JSC “Atomenergoproekt”, 7 h. 1 Bakuninskaya st. st., Moscow, 107996, Russia
a
Abstract Abstract Purpose of I&C (instrumentation and control) system is monitor and control of processing procedure and equipment for reaching main goal nuclear power plantand (NPP) – electric generation providing nuclear and radiological safety and Purpose of of I&C (instrumentation control) systemenergy is monitor and control of processing procedure and equipment foreconomic reaching efficiency processing procedure. One –ofelectric the main functions of NPP I&C system is and control of technological processes in main goal of nuclear power plant (NPP) energy generation providing nuclear radiological safety and economic nuclear unitofequipment automaticOne regulators optimality of electric energyisgeneration Algorithms of I&C efficiency processingvia procedure. of thes providing main functions of NPP I&C system control ofprocess. technological processes in systems, especially lawsviaofautomatic control, regulators are realized on hardware and of software complex TPTS. A process. great number umber of automatic nuclear unit equipment s providing optimality electriccomp energy generation Algorithms of I&C regulators are used inlaws process of electric generation on NPP. nuclear unitsTPTS. are standard (they have of same reactor systems, especially of control, areenergy realized on hardware andDespite software comp complex A great number umber automatic system project), have unique characteristics – nuclear units Despite differ by technological of reactor system regulators are usedthey in process of electric energy generation on NPP. nuclear units are characteristics standard (they have same reactor equipment and technological equipment providing– electr electric ic energy generation process control. That is why parameters of system project), they have unique characteristics nuclear units differ by technological characteristics of reactor system respective regulators equipment for each nuclear unit are corrected aftergeneration theoreticalprocess calculations in nuclear checkout pprocess rocess equipment automatic and technological providing electr electric ic energy control. That isunit why parameters of that cause automatic specific economic losses. solving ng this problem in this paper modern methods NPP automatic respective regulatorsand forenergetic each nuclear unitFor are solvi corrected after theoretical calculations in nuclear unitofcheckout pprocess rocess regulators setting are as For “intelligent” control system on neural networks and and that cause parameters specific economic andconsidered energetic such losses. solvi solving ng this problem in (based this paper modern methods offuzzy NPP logic) automatic using of analytical simulators of considered NPP nuclear units, which are us used ed for system researches of reactor system physics researches of regulators parameters setting are such as “intelligent” control (based on neural networks andand fuzzy logic) and behavior of technological equipment nuclearwhich unit. are us using of analytical simulators of NPPrespective nuclear units, used ed for researches of reactor system physics and researches of behavior of technological equipment respective nuclear unit. © 2019 The Authors. Published by Elsevier B.V. © 2018 The Authors. Published by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND © 2019 The Authors. by Elsevier B.V. ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review review under responsibility of the the scientific committee of the 9th Annual International Conference on Biologically Inspired This is an open access article under BY-NC-ND ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the CC scientific committee of the 9th Annual International Conference on Biologically Inspired Cognitive Architectures. Peer-review review under responsibility of the scientific committee of the 9th Annual International Conference on Biologically Inspired Cognitive Architectures. Cognitive Architectures.
Keywords: nuclear power plant; control system; fuzzy logic; neural network; analytical simulator; automatic regulator Keywords: nuclear power plant; control system; fuzzy logic; neural network; analytical simulator; automatic regulator
* Corresponding author. Tel.: +7-916-004-39-50. E-mail address:author. [email protected] * Corresponding Tel.: +7-916-004-39-50. E-mail address: [email protected] 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access under the CC by BY-NC-ND NDB.V. license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2019 The article Authors. Published Elsevier Peer-review review under responsibility of the committee of the(https://creativecommons.org/licenses/by-nc-nd/4.0/) 9th Annual International Conference on Biologically Inspired C Cognitive ognitive This is an open access article under thescientific CC BY-NC-ND ND license Architectures. Peer-review review under responsibility of the scientific committee of the 9th Annual International Conference on Biologically Inspired C Cognitive ognitive Architectures. 1877-0509 © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 9th Annual International Conference on Biologically Inspired Cognitive Architectures. 10.1016/j.procs.2018.11.070
Vasilij Volodin et al. / Procedia Computer Science 145 (2018) 635–640 Vasilij Volodin et al. / Procedia Computer Science 00 (2019) 000–000
636 2
1. Introduction Nomenclature GET-R HSC I&C MCR NPP PID SG TPTS
computer-aided design system of algorithms of instrumentation and control system of nuclear power plants hardware and software complex Instrumentation and Control main control room nuclear power plant proportional-integral-differential regulator steam generator hardware and software tools for instrumentation and control system applied at nuclear power plants and developed by Dukhov All-Russian Scientific Research Institute of Automation
Each NPP unit has digital control and instrumentation system. Purpose of NPP I&C system is monitor and control of processing procedure and equipment for reaching main goal of NPP – electric energy generation providing nuclear and radiological safety and economic efficiency of processing procedure [1]. I&C system includes control subsystems divided by functional purpose. I&C system has hierarchical structure and consists of following levels: level of liaising with technological object of control, level of lower automation, top level (human-machine interface) – top unit level of monitor and control. Most of the algorithms of NPP I&C systems, especially laws of control, are realized on hardware and software complex TPTS through computer-aided design system GET-R [2]. Functional modules racks perform acquisition and primary processing of input analog and discrete signals from sensors of technological parameters, proceed necessary calculations, and also perform automatic regulator and feedback and remote control such actuators as pump, slide-type valve, solenoid coil, switch, control valve. One of the main functions of NPP I&C system is control of technological processes in nuclear unit equipment via automatic regulators providing optimality of electric energy generation process. Automatic regulators provide keeping of regulating quantity in prescribed limits in stationary modes and desired characteristics of control performance in transient modes [3]. 2. Automatic regulators of NPP A great number of automatic regulators are used in process of electric energy generation on NPP. All automatic regulators can be divided into 4 groups. Automatic regulators providing control of reactor power, turbine power and generator power in basic mode and in tracking mode belong to the first group. Automatic regulators providing preset value of technological parameters of nuclear unit in transit modes caused by emergency protection system or preventive protection system belong to the second group. Automatic regulators providing preset parameters of steam generators (SG) feeding on the power levels close to nominal belong to the third group. Automatic regulators providing preset technological parameters in routine shots and shutdowns of nuclear unit including service steam belong to the fourth group. For example, this division for nuclear unit with fast fission reactor looks like this: Main automatic regulators: automatic regulator of nuclear unit power, automatic regulator of reactor power, automatic regulator of reactor coolant pump rate of turn, automatic regulator of SG feeding, automatic regulator of turbine rotor rate of turn, automatic regulator of steam pressure before turbine valves; Emergency and stand-by mode automatic regulators: automatic regulators of pressure of fast reduction system with discharge of steam into the condenser, automatic regulators of Na temperature on the output of secondary heat exchanger; Automatic regulators of SG feeding parameters: automatic regulator of deaerator level, automatic level regulator of the third step low pressure heater and etc.; Starting and own needs automatic regulators: starting automatic regulator of SG feeding, automatic regulator of pressure in evaporator plant collector etc.
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3. Tuning of automatic regulators of NPP units Set of procedures is carried out before acceptance of NPP unit in industrial operation, including prelaunch works, physical and power launches [4]. Operation of all subsystems of NPP I&C system is checked as part of prelaunch works (also in trial performance) with fixation and reduction of all identified issues, which includes tuning of NPP units automatic regulators parameters, which performs manually [5]. NPP unit is a complex control object in view of difficulty of electricity generation technological process, a great number of control technological parameters and equipments, and also in view of high requirements for quality and reliability of NPP I&C system [6]. Also regulators manual tuning process is complicated by the fact, that NPP I&C system is multi-loop control system, i.e. there are cross couplings between different control loops [7]. Coupling between control loop of neutron flux power and control loop of generator output of turbine ([8]) or coupling between control loop of feed water flow and control loop of pH index of feed water before coagulation contact tank ([9]) are fine examples of last statement. Consequently, problem of creation of methods of NPP units automatic regulators parameters tuning arises in order to reduce input time of NPP unit into industrial operation. Authors consider applicability of approaches to a problem solution, based on fuzzy logic, neural networks and analytical simulators of NPP units. 4. Methods of tuning of automatic regulators of NPP units 4.1. Fuzzy logic and neural networks Despite the fact that basis of “intelligent” control systems was invented by L. Zadeh in 1965 [10], concept of fuzzy logic and neural networks was not popular for NPP I&C system design neither in USSR and Russian Federation nor in foreign countries. However, proven experience of usage of “intelligent” control systems in other branches of industry ([11]) caused a wave of interest to this methods under their usage in NPP I&C system designing. There are mostly papers, in which investigates applicability of given methods to certain subsystems of NPP I&C system designing – for example, control system of feed water level in SG of control system of neutron flux power [12-14]. There are two ways of fuzzy logic and neural networks usage in NPP I&C system designing [15]. The first is the design of fuzzy regulator or the regulator, based on neural network [13, 14, 16-19]. However, this way connected with actual HSC of NPP I&C system replacement, therefore this method does not cause interest within the framework of problem under consideration. The second way is design of parameters tune module of classical PIDregulators used on NPP, based on neural network and fuzzy logic [12, 20]. However, NPP unit is complex, multiloop and nonlinear control object; therefore, it is impossible to consider all disturbances, which significantly influence on quality of control process. Another difficulty is in fact, that automatic regulators must be set up for all operation modes of NPP unit – nominal level of power, minimal controlled level of power etc. In view of difficulty of given problem two regulators (starting regulator and mandatory regulator) are used in some control loops – for example, control loop of feed water level in SG or control loop of coolant level in pressurizer. Another difficulty of NPP unit automatic regulators tuning is in fact, that each NPP unit is unique because typical units (with same reactor plant project) differ in technological equipment characteristics and quantity, in arrangement of adjusting controls and measure points that lead to arising of problem under consider for every unit. Abovementioned difficulties lead to the fact that a great number of linguistic rules, formed by expert, will require for record all cross couplings between control loops and for providing required control quality in all power ranges of NPP unit via fuzzy logic. Same difficulties appears also in the approach, based on neural networks. Besides “intelligent” control systems should be used with no full information about control object and its dynamics, however, it is not so in case of NPP – technical project and safety justification report with full information about control object are developed for each NPP unit. Therefore, authors propose following approach to a problem solution. 4.2. Analytical and full sized simulators Another approach to the considered problem is usage of analytical and full sized simulators of NPP units, which are used for researches of reactor system physics and researches of behaviour of technological equipment respective nuclear unit. For each unit his own analytical simulator with considering his unique characteristics is developed:
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parameters of nuclear reactor core, quantity and power of reactor coolant pumps, delivery pumps, condenser pumps, base areas and heights of reservoirs, characteristics of transformers and turbine, parameters of SG etc. Analytical simulator – hardware and software modelling complex, which is developed for training and proficiency maintaining of NPP main control room (MCR) operators, with using full sized mathematical model of nuclear unit working in real time mode. There is an analytical simulator of reserve control room of Kalininskaya NPP unit 4 on figure 1.
Fig. 1 – Analytical simulator of reserve control room of Kalininskaya NPP unit 4
In addition, for automatic regulators settings it is suggested to use full sized simulators of nuclear units because they contain mathematical model similar to mathematical model of analytical simulator working in real time mode. Full sized simulator – hardware and software modelling complex, which is developed for professional training of NPP MCR operators with using full sized model of MCR and complex all mode mathematical model of nuclear unit working in real time mode. The full sized simulator should correspond to the systems and equipment of nuclear unitprototype and provide in real time mode simulation of all operating modes of NPP (normal operation modes, transient modes, normal operation failures, design basis accidents and non-project accidents to simulation limits). There is full sized simulator of Kalininskaya NPP unit 4 containing workstations of reactor part chief engineer and turbine part chief engineer with respective segmented console, and workstation of unit supervisor on figure 2.
Fig. 2 – Full sized simulator of Kalininskaya NPP unit 4
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Approach to a NPP unit automatic regulators parameters tuning via simulators is the following. It is necessary to develop software, which will integrate into HSC of analytical simulator of respective NPP unit. Parametric identification of control object and following NPP units automatic regulators parameters setting with methods of optimal control theory in accordance with pre-set performance control criterion of technological processes on NPP unit will be performed via this software. Advantage of this approach is in fact, that developed method will be universal and verified on respective analytical simulators. “Intelligent” control systems also can be verified on this simulators, but high complexity of designing of linguistic rules for all power ranges of NPP unit makes this approach possible for some subsystems of NPP I&C system, but not for all regulators of NPP unit. 5. Conclusion Timeliness of considered problem is in fact, that there are no methods of NPP units automatic regulators parameters setting before checkout process. For solving this problem “intelligent” control systems, based on fuzzy logic and neural networks, and analytical and full sized simulators of NPP units can be used. NPP unit is the complex, nonlinear and multi-loop control object that lead to high complexity of making of linguistic rules for control systems, based on fuzzy logic, and high difficulty of neural network setting. In view of uniqueness of typical units is suggested to set up automatic regulators parameters via respective analytical simulator for each NPP unit because usage of modules of fuzzy logic and neural networks for this problem is greatly complicated by differences of characteristics and quantity of technological equipment. In farther works, it is assumed to establish correspondence between automatic regulators parameters and parameters of technological equipment, which is control objects, also to develop methods of regulators parameters tuning and software for their realization. This approach will allow tuning NPP automatic regulators without energetic and economic losses, which is occurred due to manual tuning of NPP regulators in NPP checkout process. References [1] Zverkov V.V. Avtomatizirovannaya sistema upravleniya tehnologicheskimi protsessami AES [Automated process control system for nuclear power plants]. Moscow: NIYaU MIFI Publ., 2014. [2] Zverkov V.V. Programmno-tehnicheskiye kompleksy ASU TP AES. Funktsionalnye i strukturnye resheniya [Hardware and software complexes of NPP I&C system]. Moscow: NIYaU MIFI Publ., 2018. [3] Potapenko P.T. Dinamika yadernogo reaktora [Dynamics of nuclear reactor]. Moscow: MIFI Publ., 1989. [4] Ostrejkovskij V.A. Ekspluatatsiya atomnyh stantsij [Operation of nuclear power plants]. Moscow: Energoatomizdat Publ., 1999. [5] Saakov E.S., Ryasnyj S.I. Vvod v ekspluatatsiyu energoblokov AES [Start-up of nuclear power plant units]. Moscow: Energoatomizdat Publ., 2007. [6] Stefani E.P. Osnovy postrojeniya ASU TP [Basics of I&C system design]. Moscow: Energoatomizdat Publ., 1982. [7] Tsykunov A.M. Robastnoye upravleniye obyektami s posledejstviem [Robust control of objects with persistent]. Moscow: FIZMATLIT Publ., 2014. [8] Vygovskij S.B., Ryabov N.O., Semenov A.A., Chernov E.V., Bogachek L.N. Fizicheskiye i konstruktsionnye osobennosti yadernyh energeticheskih ustanovok s VVER [Physical and construction features of nuclear power plants with VVER]. Moscow: NIYaU MIFI Publ., 2011. [9] Petrova T.I., Voronov V.N., Larin B.M. Tehnologiya organizatsii vodno-himicheskogo rezhima atomnyh elektrostantsij [Technology of nuclear power plants water chemistry organization]. Moscow: Izdatelskij dom MEI Publ., 2012. [10] Zade L. Ponyatie lingvisticheskoj peremennoj i ego priminenie k prinyatiyu priblizhennyh reshenij [Fuzzy sets]. Moscow: Mir Publ., 1976. [11] Pegat A. Nechetkoe modelirovanie i upravlenie [Fuzzy modeling and control]. Moscow: BINOM. Laboratoriya znanij Publ., 2018. [12] Al Masri Husam Fayiz (2017) “Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants.” J. of Physics: Conf. Series 781: 0112052. [13] Bartosz Puchalski, Kazimierz Duzinkiewicz, and Tomasz Rutkowski (2015) “Multi-region fuzzy logic controller with local PID controllers for U-tube steam generator in nuclear power plant.” Archives of Control Sciences 25 (4): 429-444. [14] Arab-Alibeik H., Setayeshi S. (2005) “Adaptive control of a PWR core power using neural networks.” Annals of Nuclear Energy 32: 588605. [15] Denisenko V.V. Kompyuternoe upravlenie tehnologichesckim protsessom, eksperimentom, oborudovaniem [Computer control of technological process, experiment and equipment]. Moscow: Goryachaya liniya – Telekom Publ., 2014. [16] Chris Brown, Hossam A. Gabbar (2014) “Fuzzy logic control for improved pressurizer systems in nuclear power plants.” Annals of Nuclear Energy 72: 461-466.
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[17] Mauro Vitor de Oliveira, José Carlos Soares de Almeida (2013) “Application of artificial intelligence techniques in modeling and control of a nuclear power plant pressurizer system.” Progress in Nuclear Energy 63: 71-85. [18] Jun-Jen Lu, Hsuan-Han Huang, Hwai-Pwu Chou (2015) “Evaluation of an FPGA-based fuzzy logic control of feed-water for ABWR under automatic power regulating.” Progress in Nuclear Energy 79: 22-31. [19] Habibiyan H., Setayeshi S., Arab-Alibeik H. (2004) “A fuzzy-gain-scheduled neural controller for nuclear steam generators.” Annals of Nuclear Energy 31: 1765-1781. [20] Cheng Liu, Jin-Feng Peng, Fu-Yu Zhao, Chong Li (2009) “Design and optimization of fuzzy-PID controller for the nuclear reactor power control.” Nuclear Engineering and Design 239: 2311-2316.